Currently, engines and combustors generally run at high temperatures, which can lead to problems such as engine knocking, flame stability, soot and NOx emissions as well as difficulty in combustion control. In spite of extensive attempts to operate engines at low temperature environments for mitigated emissions and improved engine efficiencies, low temperature combustion and fuel oxidation below 1000 K have been found to be difficult to achieve and/or unstable for engines and fuel processing.
The concept of a cool flame has been around for more than a century. After the accidental discovery of cool flames in 1882 (see, e.g., Reference 13), cool flames have been regarded as a processes that can cause engine knock, motivating extensive studies on large hydrocarbon low temperature chemistries. (See, e.g., References 14 and 15). Three conventional experimental approaches for the past studies of cool flames include: 1) heated burner, 2) heated flow reactor and 3) jet-stirred reactor. (See, e.g., Reference 16). Despite of the ambiguous boundary conditions and limited operation range in a heated burner, the convenience of optical accessibility made it possible to measure emission spectroscopy, revealing the excited formaldehyde (e.g., CH2O*) as a source of the pale bluish chemiluminescence of a cool flame. (See, e.g., Reference 16). Heated flow reactor experiments have provided oxidative chemical kinetic behaviors of hydrocarbon (e.g., liquid fuels) at given temperatures (e.g., 500-1000 K) of cool flames. (See, e.g., References 13-16).
Recently, based on the heated flow reactor concept, cool flames have been observed in a micro-channel flow, constraining the auto-thermal acceleration by the extensive wall heat loss. (See, e.g., Reference 21). Various types of preheated jet-stirred reactors also have been utilized to investigate the chemical kinetic characteristics of cool flame chemistry. (See, e.g. Reference 16). Generally, in all above experiments, external heating and wall heat losses ought to be provided to establish cool flames, mating the thermal and chemistry coupling with very complicated wall interaction. As a result, detailed and fundamental understandings of cool flame behaviors have not been well established.
Additionally, all of the previous cool flame studies have focused on homogeneous fuel/air pre-mixtures. A recent experiment of droplet combustion in microgravity has shown that a cool flame might be established even in the diffusive system, hypothesizing the existence of cool diffusion flame after radiation-controlled extinction (see, e.g., Reference 22) with the aid of numerical simulation. (See, e.g., Reference 23). Although, the numerical simulation was able to capture the global trend of droplet flame extinction, and subsequent formation of cool diffusion flame, the detailed structure of cool diffusion flame has not yet been revealed. As such, cool flame dynamics remain mysterious and the fidelity of cool flame chemistry remains unknown. Furthermore, this experimental observation was performed in a sophisticated environment, microgravity in NASA international space station, and is not applicable to the realization of cool flames in engine-relevant conditions.
One of the main challenges in establishing a self-sustaining cool flame can be that at low temperature, the cool flame induction chemistry for the radical branching can be too slow compared to the flow residence time in a practical combustor. On the other hand, at higher temperature, the radical branching can become so fast that a cool flame will transit to a hot flame rapidly. (See, e.g., Reference 24). As a result, a cool flame may not be stable without heat loss to the wall. Therefore, the only way to create a self-sustaining cool flame can be to significantly accelerate the chain-branching process at low temperature by providing new reaction pathways.
Recent progresses in plasma-assisted combustion in counterflow flames (see, e.g., References 25 and 26) provide some information on how to accelerate the cool flame induction chemistry at a low temperature. By producing active radicals, such as atomic oxygen, direct in-situ plasma discharge between two nozzles in counterflow burner can stabilize diffusion flames at flame temperatures below 1000 K at a flow residence time of 10 ms, and can also modify the ignition/extinction S-curve. Unfortunately, direct plasma discharge can make the study of low temperature combustion chemistry more complicated. Thus, to isolate the plasma-flame coupling, ozone was used as an atomic oxygen carrier to enhance flame stabilization and ignition. (See, e.g., References 27 and 28). Ozone has been also frequently utilized to reduce the time scale of induction chemistry, and activate the low temperature chemistry in tubular (see, e.g., Reference 29) and jet-stirred reactors. (See, e.g., Reference 30). Those studies suggest that one might be able to observe a self-sustaining cool flame using ozone. As the recent advanced concepts of engine design (see, e.g., References 31-33) appear to heavily relying on the low temperature combustion, it can be pre-requisite to understand the chemical kinetic mechanism and flame dynamics at the regime of low temperature chemistry. Consequently, a fundamental challenge can be how to develop an experimental cool flame platform with well-defined chemical and flow boundary conditions, such that both the global properties and the detailed chemical kinetics of cool flame can be simultaneously investigated and/or determined.
Intermittency, low energy density and difficulty in electricity storage can be among significant challenges of the sustainable electric power triangle. (See, e.g., FIG. 17). An increase of energy efficiency, fuel flexibility and capability of distributed power generation via integration of fuel-flexible solid oxide fuel cells (“SOFCs”) and micro gas turbines can be beneficial to the future development of the sustainable electric power triangle. (See, e.g., Reference 1). SOFCs can have over 60% efficiency in converting fuel chemical potential energy into electricity, and this can be further increased via integration with gas turbines by using the energy in the exhaust gas of SOFCs. Moreover, SOFCs have almost zero NOx and soot emissions.
However, one of the important challenges of SOFCs can be the lack of fuel flexibility and coke formation (e.g., carbon deposits) for large hydrocarbon fuels. (See, e.g., References 2 and 3). FIG. 18 illustrates the operating principles of SOFCs in a planar geometry. (See, e.g., Reference 4). A SOFC can include a porous cathode and anode separated by a solid electrolyte. The cathode material can include lanthanum-containing perovskite oxides such as the lanthanum strontium manganate (e.g., LaxSr(1−x)MnO3). Ceria- and zirconia-based oxides such as yttrium-stabilized zirconia (“YSZ”) can generally be used as the solid electrolyte. The typical anode material can be a composite of ceramic and metal such as a nickel-YSZ composite. (See, e.g., Reference 5). The cathode catalyzes oxygen to form oxide ions (O2—) (Eq. (1A)). The formed O2— ions can travel across the solid electrolyte to the porous SOFC anode and oxidize chemical fuels such as carbon monoxide (“CO”) and hydrogen (“H2”) and release electrons to produce electricity (Eq. (2A)).Cathode: O2+4e−→2O2—  (1A)Anode: H2+O2—→H2O+2e−  (2A)CnH2n+2→nC+(n+1)H2  (3A)
To increase the O2— ion transport and the fuel decomposition and oxidation, SOFCs have to be operated at high temperatures (e.g., approximately 900° C. or higher). Unfortunately, nickel-based catalysts are very prone to form coke for large hydrocarbon fuels (e.g., shale gas liquids, gasoline and biodiesels) (Eq.(3A)) and even for high concentrations of methane (e.g., over 20%) (see, e.g., References 1-3), limiting the most desirable fuel of SOFCs to H2/CO.
To increase fuel flexibility, a separate fuel reformer to produce H2/CO from large hydrocarbons can be needed. (See, e.g., References 2-6). However, the high temperature (e.g., about 550-600° C.), endothermic reforming process can reduce the energy efficiency and increase the cost. Moreover, to reduce coke and catalyst deactivation, expensive catalysts such as Ru/CeO2 can be frequently used for catalytic partial oxidation (“CPDX”), direct steam reforming (“DSR”), or autothermal reforming (“ATR”). (See, e.g., References 3-6). Unfortunately, both CPDX and DSR can be sensitive to fuels and H2O content and have low energy efficiency. A small variation in the fuel stream can result in significant changes in catalyst bed temperature, product yields and coking. (See, e.g., Reference 7).
Moderate or Intense Low-oxygen Dilution (“MILD” or flameless) combustion has been investigated as a combustion concept to reduce pollutant emissions. (See, e.g., References 59 and 60). The basic concept of MILD combustion is to use excessive diluents to reduce fuel and oxygen concentrations below its flammable limit (see, e.g., References 61 and 62), and raise the oxidizer stream temperature to autoignition temperature of the fuel. MILD or flameless combustion occurs when preheated and highly diluted oxidizer (e.g., approximately 1300 K) and fuel are rapidly mixed. The rapid mixing associated with highly diluted reactants and reduced peak flame temperature can change the conventional diffusion flame regime to ignition dominated MILD or flameless combustion regime. This ignition-dominated MILD combustion process can facilitate reactants and intermediate species to leak through the reaction zone and therefore enlarges the combustion zone. As a result, the associated heat release can be distributed onto a larger volume with a significantly decreased peak flame temperature and thus preventing the formation of NOx. Although MILD combustion has the potential to reduce emissions in applications, it needs high preheating temperatures (e.g., over about 1300 K for methane) and high fuel/air dilutions. Due to the nonlinearity of combustion chemistry under such extreme conditions, chemically-induced flame instability can arise. To mitigate these oscillations and realize steady MILD combustion conditions, an external control, such as a closed-loop controller can be used (see, e.g., Reference 63) but has limitations in terms of response time.
Plasma assisted combustion has the potential to achieve steady MILD combustion by manipulating the chemical time scale (e.g., ignition delay time) and reducing the auto-ignition temperature. The influence of electron impact on fuel chemistry has already been extensively studied. (See, e.g., References 64-66). Using a counterflow configuration, it has been shown that plasma discharge can produce a direct ignition to flame transition regime without extinction limit (see, e.g., Reference 67) and even cool flames. (See, e.g., References 68 and 69). These studies suggest that an optimized plasma discharge, cool flame initiation process can lead to steady MILD combustion at even lower temperature and concentration conditions by reducing the ignition time via thermal and kinetic enhancement processes.
In order to understand and predict the turbulence-chemistry interactions within low temperature (e.g., 700K) to intermediate temperature (e.g., 1100K) range more accurately, there has been increasing interest in developing detailed kinetic mechanisms including low temperature chemistry, which involves hundreds of species and thousands of reactions. For example, a detailed n-heptane mechanism (see, e.g., Reference 77) has about 1034 species and about 4236 reactions and a recent jet fuel surrogate model (see, e.g., Reference 78) has more than two thousands species and eight thousands reactions. However, the large detailed chemical mechanisms results in great challenges in combustion modeling. Even with the availability of supercomputing capability at petascale and beyond, the numerical simulation with such large kinetic mechanisms still remains difficult.
The first difficulty comes from the large and stiff ODE system which governs the chemical reactions. In a chemical reaction system, the characteristic time of different species can vary from millisecond to picosecond, and even beyond. In order to deal with the stiffness of the ODE system, the traditional VODE (see, e.g., Reference 79) method can be usually applied. However, the computation time of a VODE solver increases as cubic of the number of species due to the Jacobin matrix decomposition. Therefore, with a large detailed mechanism, the numerical simulation can be almost impossible. In order to utilize the detailed kinetic models in combustion modeling, researchers either reduce the stiffness of the ODE system, such as the computational singular perturbation (“CSP”) method (see, e.g., Reference 80), the intrinsic low-dimensional manifold (“ILDM”) method (see, e.g., Reference 81), the hybrid multi-timescale (“HMTS”) method(see, e.g., Reference 82) and the dynamic stiffness removal method (see, e.g., Reference 83), or decrease the number of the species in the mechanisms, such as the visualization method (see, e.g., Reference 84), the multi-generation path flux analysis (“PFA”) method(see, e.g., Reference 85), the direct relation graph (“DRG”) method(see, e.g., Reference 86) and the DRG with error propagation (“DRGEP”) method. (See, e.g., Reference 87). The dynamic adaptive chemistry (“CO-DAC”) method (see, e.g., Reference 88) integrated with the HMTS method combined these two approaches and successfully addressed the first difficulty. In the CO-DAC method, correlated reduced mechanisms in time and space coordinate can be generated dynamically on the fly from the detailed kinetic mechanism by using the multi-generation PFA method. Then, the HMTS method can be applied to solve the chemical reactions based on the local reduced mechanisms. The efficiency and accuracy of the CO-DAC method has been previously demonstrated. (See, e.g., Reference 88).
The second difficulty in utilizing a large detailed chemical mechanism in combustion modeling can be from the calculations of transport properties. In a combustion process, the significant variations of the temperature and concentrations of species and radicals can result in the deviations of the transport coefficients from the initial values. Therefore, the mass diffusivities, heat conductivities and viscosities have to be updated during the calculations according to diffusion models. The Boltzmann's equation of kinetic theory (see, e.g., References 89 and 90) can provide the most rigorous Maxwell-Stefan multicomponent diffusion model. (See, e.g., References 91 and 92). However, it can be computationally prohibitive to employ the multicomponent diffusion model in the combustion modeling with large chemical mechanism due to the huge computational cost by matrix inversion. Therefore, a mixture-averaged diffusion model developed by the first-order perturbations of the Boltzmann equation following the Chapman-Cowling procedure 89 and 93-95) can be preferred. It can provide good accuracy with substantially lower computational cost for most combustion systems. In a recent examination of the computation of laminar flames, the mixture averaged diffusion model can be regarded as the de facto standard in combustion modeling. (See, e.g., Reference 96)
Recently, a reduced multicomponent diffusion model was described. (See, e.g., Reference 97). In this model, the diffusion coefficients of the important species can be calculated by the more accurate multicomponent model, and for the rest of the unimportant species, the mixture-averaged model can be applied. This model gives relatively accurate prediction of the diffusion coefficients compared with the mixture-averaged model and reduced the computational cost by 80% compared with the multicomponent model. But it can be still much slower than the mixture averaged model.
Even if the mixture averaged model, and the reduced multicomponent model, can be several orders of magnitude faster than the multicomponent model, they can still be too time consuming to be used for the calculations of convection and diffusion fluxes in a large scale numerical simulation. This problem becomes more significant when the chemistry may not be the most time consuming part anymore such as when a computationally efficient chemistry integrator such as CO-DAC method can be used. (See, e.g., Reference 88). The computational cost of the transport terms, mostly the diffusion coefficients, becomes the dominant time consuming part in the modeling. Therefore, further reducing the computation cost of diffusion coefficients can be of great importance when a large kinetic mechanism can be used.
Thus, it may be beneficial to provide an exemplary process for generating/establishing a cool flame, which can address and/or overcome at least some of the deficiencies described herein above.